Semi-automated system for real-time wound image segmentation and photogrammetry on a mobile platform

ABSTRACT

In one example embodiment, a wound imaging system includes a user interface, a computer processor, and an active contouring module. The user interface is configured to display an image of a wound acquired by the wound imaging system and selectively receive inputs from a user defining an initial perimeter of the wound. An active contouring module is configured to operate on the computer processor to receive the inputs defining the initial perimeter of the wound, identify features of the image on opposing sides of the initial perimeter of the wound, and identify an actual perimeter of the wound based on the initial perimeter of the wound and the identified features. The user interface is further configured to display, on the image of the wound, the actual perimeter of the wound as identified by the active contouring module and selectively receive inputs from the user to modify the actual perimeter of the wound.

RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.16/986,878, entitled “SEMI-AUTOMATED SYSTEM FOR REAL-TIME WOUND IMAGESEGMENTATION AND PHOTOGRAMMETRY ON A MOBILE PLATFORM”, filed Aug. 6,2020, which is a Continuation of U.S. patent application Ser. No.16/308,158, entitled “SEMI-AUTOMATED SYSTEM FOR REAL-TIME WOUND IMAGESEGMENTATION AND PHOTOGRAMMETRY ON A MOBILE PLATFORM”, filed Dec. 7,2018, which is a National Stage application claiming priority to PCTApplication No. PCT/US2017/039214, entitled “SEMI-AUTOMATED SYSTEM FORREAL-TIME WOUND IMAGE SEGMENTATION AND PHOTOGRAMMETRY ON A MOBILEPLATFORM”, filed Jun. 26, 2017, which claims the benefit, under 35 USC119(e), of the filing of U.S. Provisional Patent Application No.62/355,780, entitled “SEMI-AUTOMATED SYSTEM FOR REAL-TIME WOUND IMAGESEGMENTATION AND PHOTOGRAMMETRY ON A MOBILE PLATFORM,” filed Jun. 28,2016, which are incorporated herein by reference for all purposes.

TECHNICAL FIELD

The invention set forth in the appended claims relates generally totissue treatment systems. More particularly, but without limitation, thepresent disclosure relates to systems and methods for accomplishingacquisition and processing of wound images, as well as photogrammetry.

BACKGROUND

A wound is generally defined as a break in the epithelial integrity ofthe skin. Such an injury, however, may be much deeper, including thedermis, subcutaneous tissue, fascia, muscle, and even bone. Proper woundhealing is a highly complex, dynamic, and coordinated series of stepsleading to tissue repair. Acute wound healing is a dynamic processinvolving both resident and migratory cell populations acting in acoordinated manner within the extra-cellular matrix environment torepair the injured tissues. Some wounds fail to heal in this manner (fora variety of reasons) and may be referred to as chronic wounds.

Following tissue injury, the coordinated healing of a wound willtypically involve four overlapping but well-defined phases: hemostasis,inflammation, proliferation, and remodeling. Hemostasis involves thefirst steps in wound response and repair which are bleeding,coagulation, and platelet and complement activation. Inflammation peaksnear the end of the first day. Cell proliferation occurs over the next7-30 days and involves the time period over which wound areameasurements may be of most benefit. During this time, fibroplasia,angiogenesis, re-epithelialization, and extra-cellular matrix synthesisoccur. The initial collagen formation in a wound typically peaks inapproximately 7 days. The wound re-epithelialization occurs in about 48hours under optimal conditions, at which time the wound may becompletely sealed. A healing wound may have 15% to 20% of full tensilestrength at 3 weeks and 60% of full strength at 4 months. After thefirst month, a degradation and remodeling stage begins, whereincellularity and vascularity decrease and tensile strength increases.Formation of a mature scar often requires 6 to 12 months.

There are various wound parameters that may assist a clinician indetermining and tracking healing progress of a wound. For example, wounddimensions, including wound area and volume measurements, may provide aclinician with knowledge as to whether or not a wound is healing and, ifthe wound is healing, how rapidly the wound is healing. Wound assessmentis an important process to properly treating a wound, as improper orincomplete assessment may result in a wide variety of complications.

While wound measurements may provide valuable parameters for helping aclinician assess wound healing progress, the size of the wound may notprovide a clinician with a full picture to fully assess whether or how awound is healing. For example, while the size of a wound may be reducedduring treatment, certain parts of a wound may become infected. Aclinician may often-times examine wound tissue for its color and textureto determine how a wound is healing. Wound tissue includes a wound bedand peri-wound areas or wound edges. Health of a wound may be determinedby color of tissue, with certain problems often presenting with distinctcolors at the wound. For example, normal granulation tissue typicallyhas a red, shiny textured appearance and bleeds readily, whereasnecrotic tissue (i.e., dead tissue) may either be yellow-gray and soft,generally known as “slough” tissue, or hard and blackish-brown in color,generally known as “eschar” tissue. A clinician may observe and monitorthese and other wound tissues to determine wound healing progress of theoverall wound, as well as specific wound regions.

Because wound treatment can be costly in both materials and professionalcare time, a treatment that is based on an accurate assessment of thewound and the wound healing process can be essential.

SUMMARY

New and useful systems, apparatuses, and methods for wound imagesegmentation are set forth in the appended claims. Illustrativeembodiments are also provided to enable a person skilled in the art tomake and use the claimed subject matter.

For example, in some embodiments, systems and methods according to theprinciples of the present disclosure provide user-assisted woundperimeter identification implementing active contouring. A touchscreenis provided for allowing a user to establish an initial, rough outlinearound a perimeter of a wound in an acquired image displayed on thetouchscreen. An active contour process is then applied to the imageusing the initial outline provided by the user to accurately identifythe actual wound perimeter for further image processing.

In some embodiments, a wound imaging system may include a non-transitorycomputer readable medium for storing a wound image, a computerprocessor, and an active contouring module. The computer processor maybe configured to output for display the wound image, as well asselectively receive via a user interface a first input from a userdefining an initial perimeter of a wound displayed in the wound image.The computer processor may be further configured to execute an activecontouring module configured to identify features of the wound image onopposing sides of the initial perimeter of the wound and to identify anactual perimeter of the wound based on the first input and theidentified features. The computer processor may also be furtherconfigured to output for display a graphical representation of theidentified actual perimeter of the wound. Additionally, the computerprocessor may be further configured to receive a second input from theuser to modify or confirm the actual perimeter of the wound.

Alternatively, other example embodiments may implement a wound imagingmethod that includes displaying a wound image stored on a non-transitorycomputer readable medium, selectively receiving a first input from auser defining an initial perimeter of a wound displayed in the woundimage, executing, by a computer processor, an active contouring moduleconfigured to identify features of the wound image on opposing sides ofthe initial perimeter of the wound, determining, by the computerprocessor, an actual perimeter of the wound displayed in the wound imagebased on the initial perimeter of the wound and the identified features,outputting for display, by the computer processor, a graphicalrepresentation of the determined actual perimeter of the wound asidentified by the active contouring module, and selectively receiving,by the computer processor, a second input from the user to modify theactual perimeter of the wound.

In an example embodiment, a wound imaging system may include a computerprocessor and an active contouring module configured to be executed bythe computer processor. The computer processor may be configured tooutput for display on a touchscreen an image of a wound acquired by thewound imaging system, process a first input from a user on thetouchscreen defining an initial perimeter of the wound, the first inputincluding a plurality of points establishing a polygon around the wound,and process a second input from the user on the touchscreen indicatingthat the user has completed defining the initial perimeter. The activecontouring module may be configured to receive the first input definingthe initial perimeter of the wound and the second input indicating thatthe user has completed defining the initial perimeter, generate aninitial active contour corresponding to the initial perimeter, identifyfeatures of the image on opposing sides of the initial active contour,and, based on the identified features, iteratively modify the initialactive contour to calculate a final active contour corresponding to anactual perimeter of the wound. The touchscreen may be further configuredto display the final active contour calculated by the active contouringmodule

Objectives, advantages, and a preferred mode of making and using theclaimed subject matter may be understood best by reference to theaccompanying drawings in conjunction with the following detaileddescription of illustrative embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a therapy network in accordance with anexemplary embodiment;

FIG. 2 is an illustration of a screen shot of a graphical user interface(GUI) of a mobile wound imaging software for operation on an electronicdevice, in accordance with the exemplary embodiment of FIG. 1 ;

FIG. 3A is a perspective view illustrating additional details that maybe associated with some example embodiments of the therapy network ofFIG. 1 ;

FIG. 3B illustrates a screen shot of a graphical user interface (GUI) ofa mobile wound imaging software for operation on an electronic devicethat may be associated with some example embodiments of the therapynetwork according to FIG. 1 ;

FIG. 3C is an illustration of a screen shot of a graphical userinterface (GUI) of a mobile wound imaging software for operation on anelectronic device that may be associated with some example embodimentsof the therapy network according to FIG. 1 ;

FIG. 4 illustrates a functional diagram of a wound imaging softwareaccording to an exemplary embodiment that may be associated with someexample embodiments of the therapy network according to FIG. 1 ;

FIG. 5A illustrates an example wound image before undergoing imageprocessing in accordance with the specification;

FIG. 5B illustrates example views of the wound image of FIG. 5A afterundergoing processing to convert the wound image to alternative colorspaces;

FIG. 5C illustrates example views of the wound image of FIG. 5A afterundergoing processing to highlight specified gradients, in accordancewith the specification;

FIG. 6 illustrates example views of the result of a skin classificationoperator as applied to the wound image of FIG. 5A;

FIG. 7A shows views of example wound images which have properties thatmay confound simple wound tissue classification methods;

FIG. 7B shows additional views of example wound images which haveproperties that may confound simple wound tissue classification methods;

FIGS. 8A-8D illustrate example views associated with an exemplary imageprocessing method in accordance with the specification for an examplewound image;

FIG. 9 shows a graphical illustration of the progress of the exemplaryimage processing method for the example wound image of FIGS. 8A-8D;

FIGS. 10A-10C illustrate example views associated with an exemplaryimage processing method in accordance with the specification for anexample wound image;

FIG. 11 shows a graphical illustration of the progress of the exemplaryimage processing method for the example wound image of FIGS. 10A-10C;

FIGS. 12A-12C illustrate example views associated with an exemplaryimage processing method in accordance with the specification for anexample wound image;

FIG. 13 shows a graphical illustration of the progress of the exemplaryimage processing method for the example wound image of FIGS. 12A-12C;and

FIG. 14 illustrates a view of the result of an image filter applied toan example wound image for identifying the extents of a wound ruler.

DETAILED DESCRIPTION

The following description of example embodiments provides informationthat enables a person skilled in the art to make and use the subjectmatter set forth in the appended claims, but may omit certain detailsalready well-known in the art. The following detailed description is,therefore, to be taken as illustrative and not limiting.

The example embodiments may also be described herein with reference tospatial relationships between various elements or to the spatialorientation of various elements depicted in the attached drawings. Ingeneral, such relationships or orientation assume a frame of referenceconsistent with or relative to a patient in a position to receivetreatment. However, as should be recognized by those skilled in the art,this frame of reference is merely a descriptive expedient rather than astrict prescription.

FIG. 1 is a schematic diagram of an example embodiment of a therapynetwork 100 that can support a wound imaging and diagnostic applicationin accordance with this specification. The therapy network 100 mayinclude a clinical setting 102, which may include an environment where apatient 104 with a tissue site 106 may be evaluated and/or treated by aclinician 108. The clinician 108 may use a mobile device 110, inconjunction with the wound imaging and diagnostic application, tocapture, edit, and analyze images related to the tissue site 106.

The term “tissue site” in this context broadly refers to a wound,defect, or other treatment target located on or within tissue, includingbut not limited to, bone tissue, adipose tissue, muscle tissue, neuraltissue, dermal tissue, vascular tissue, connective tissue, cartilage,tendons, or ligaments. A wound may include chronic, acute, traumatic,subacute, and dehisced wounds, partial-thickness burns, ulcers (such asdiabetic, pressure, or venous insufficiency ulcers), flaps, and grafts,for example. The term “tissue site” may also refer to areas of anytissue that are not necessarily wounded or defective, but are insteadareas in which it may be desirable to add or promote the growth ofadditional tissue.

The term “clinician” is used herein as meaning any medical professional,user, family member of a patient, or patient who interacts or interfaceswith the various aspects of care related to a tissue site.

A mobile device for the purposes of this application may be anycombination of a computer or microprocessor. The computer ormicroprocessor may be programmed to implement one or more softwarealgorithms for achieving the functionality described in thespecification and corresponding figures. The mobile device, such asmobile device 110, may also include a communication device, and may be asmartphone, a tablet computer, or other device that is capable ofstoring a software application programmed for a specific operatingsystem (e.g., iOS, Android, and Windows). The mobile device 110 may alsoinclude an electronic display, such as a graphical user interface (GUI),for providing visual images to a user, such as a clinician or patient.The mobile device 110 may be configured to communicate with one or morenetworks 112 of the therapy network 100. In one preferred embodiment,the mobile device 110 may include a cellular modem and may be configuredto communicate with the network(s) 112 through a cellular connection. Inother embodiments, the mobile device 110 may include a Bluetooth® radioor other wireless radio technology for communicating with the network(s)112. The mobile device 110 may be configured to transmit data related tothe tissue site 106 of the patient 104.

The therapy network 100 may also include a support center 114 that maybe in communication with the mobile device 110 through network(s) 112.For example, the mobile device 110 may be configured to transmit datathrough network(s) 112 to the support center 114. The support center 114may support a wound imaging database 116. In some embodiments, thesupport center 114 may include both a clinical support center 118 and atechnical support center 120. The clinical support center 118 mayfunction as a centralized center for clinicians to contact regardingquestions they may have related to imaging of specific wounds with whichthey may be presented. The technical support center 120 may serve as acontact point for solving technical issues with use of the wound imagingand diagnostic application.

The therapy network 100 may also include other entities that maycommunicate with clinical settings, mobile devices, and support centersthrough network(s) 112. For example, the therapy network 100 may includea third party 122. In some embodiments, the third party 122 may be animage-processing vendor. Various image-processing vendors may beincluded as part of the therapy network 100, to provide expertise andsupport for wound images that may be particularly unique or challengingto process and analyze. Such image-processing vendors may offer one ormore additional software packages that may be used for processingspecific aspects of captured wound images. In these embodiments, arepresentative in the clinical support center 118 may determine that aparticular image requires the additional processing expertise offered bya specific image-processing vendor and may route the image file(s) tothat vendor. In some embodiments, the wound imaging and diagnosticapplication may prompt the user, such as the clinician, for routing theimage to the third-party vendor, or in some cases, may be configured toautomatically route the image to one or more particular image-processingvendors.

Referring to FIG. 2 , a screen shot of an illustrative graphical userinterface (GUI) 200 of an exemplary embodiment of the wound imaging anddiagnostic application is shown. As already discussed, the wound imagingand diagnostic application may operate on a mobile device, such as asmartphone. Following logging into the wound imaging and diagnosticapplication, a user, such as a clinician, may be presented with a menubar 202, which in some embodiments may be located across the bottom ofthe GUI 200. The menu bar 202 of the GUI 200 may include a number ofselectable graphical elements, including a “Your Patients” soft-button204, “Image Library” soft-button 206, “Search” soft-button 208, “ContactUs” soft-button 210, “Sign Out” soft-button 212, along with soft-buttonsassigned to any other features related to the collection, processing,and management of wound images. The user may select any of thesefunctions (i.e., your patients, image library, search, contact us, signout) to cause another GUI for performing the selected function to bepresented to the user. For example, the “Your Patients” soft-button 204may function to display a list of the patients assigned to the caseloadof the particular user, while the “Image Library” soft-button 206 maydisplay a list of wound images captured or reviewed by the user. The“Search” soft-button 208 may allow a user to find images or imageinformation with respect to a given patient. Additionally, the “ContactUs” soft-button 210 may allow a user to send questions, comments, orsuggestions about the wound imaging and diagnostic application to a teamat the clinical support center 118 and/or the technical support center120. The “Sign Out” soft-button 212 may allow a user to securely log outof the wound imaging and diagnostic application. It should be understoodthat GUI 200 is exemplary and that other and/or alternative functionsand selection elements may be provided to the user.

Still referring to the exemplary screen shot of FIG. 2 , an exemplaryillustration of the functionality of the wound imaging and diagnosticapplication is shown. For example, FIG. 2 illustrates an exampleembodiment of a patient information view 213. The patient informationview 213 may include a patient identification ribbon 214, present at thetop of the patient information view 213 of the GUI 200. The patientidentification ribbon 214 may provide secure information regarding oneor more data items related to identifying the patient. For example, thepatient identification ribbon 214 may display one or more of a patient'sname, social security number, or a unique patient identification number.

In some embodiments, the patient information view 213 may include awound information view 216. The wound information view 216 may includedata fields for descriptors related to one or more wounds of a patient.For example, an anatomical location field 218 may provide a field for adescriptive location on the body of the patient where the wound islocated. A wound etiology field 220 may provide a field for entering anddisplaying the type of wound. For example, the wound etiology field 220may indicate that the wound is a burn, surgical wound, ulcer, or othertype of wound. The wound information view 216 may also includeadditional descriptive fields related to the physical nature of thewound, such as a tissue damage field 222 and an exposed structures field224. The tissue damage field 222 may allow for a description of how manylayers of skin tissue are damaged, or in other words, the depth of thewound. The exposed structures field 224 may provide a space for listingany nearby structures at the tissue site that are exposed or otherwisepossibly affected by the wound. A wound images field 226 may be includedfor displaying a collection of images of the wound. The wound imagesfield 226 may allow for a user to tap on a particular image for afull-screen view, and may also allow the user to spread and enlarge theimage for a more detailed view of particular wound aspects. The woundimages displayed in the wound images field 226 may be images taken by anumber of individuals, including one or more clinicians and the patient.Some embodiments of the wound images field 226 of the wound informationview 216 may include functionality for transmitting the images throughthe image processing application. A message field 228 may allow forusers to send and receive secure text and picture messages using thewound imaging and diagnostic application through the network(s) 112. Forexample, messages may be sent by the clinician 108 from the mobiledevice 110 through the network(s) 112 to the support center 114.

The wound information view 216 of the patient information view 213 mayinclude a wound dimensions field 230, which may allow for dimensions ofthe wound to be entered, including length, width, and depth of thewound. The wound dimensions may be either manually entered into thewound dimensions field 230, or alternatively may be automaticallydetermined by the wound imaging and diagnostic application based onimage processing analysis of one or more images of the specific wound.The wound information view 216 may also include an other woundconditions field 232, which may be used to note any other physicalcharacteristics or complications associated with the particular woundsite. An exudate field 234 may also be included in the wound informationview 216, which may be used for documenting moisture conditions at thewound site, including a classification of the amount of exudateoriginating from the wound. Additionally, the wound information view 216may also include additional or alternative data fields, based onclinical applications and needs.

Referring to FIG. 3A, an exemplary patient environment, such as clinicalsetting 102, is shown with the patient 104 having a tissue site 106.Mobile device 110 is also shown, with an image capture device 302, whichmay be utilized to capture an image of the tissue site 106. The capturedimage may then be transmitted from the image capture device 302 to themobile device 110. The image capture device 302 may be a digital camera,mobile telephone, or any other electronic device configured to capturean image in a digital or analog format. In general, to expeditecapturing and working with an image of the tissue site 106, the imagecapture device 302 may be in the form of a digital camera that isconfigured to be physically connected to the mobile device 110 and maycommunicate with the mobile device 110 using a wired connection.Alternatively or additionally, the image capture device 302 may beconfigured to be wirelessly connected to the mobile device 110. In someembodiments, the image capture device 302 may utilize a memory device(not shown) that may be transferred between electronic devices. Thememory device may include flash memory, a memory stick, or any othermemory device with which the mobile device 110 may be compatible.

As previously discussed, the image capture device 302 may be used tocapture images which may be incorporated into the wound imaging anddiagnostic application. The captured images may then be shared amonginterested parties, such as the clinician, image processing vendors, andthe patient. Wound images captured by the image capture device 302 maybe used by the wound imaging and diagnostic application to determine andsubsequently populate one or more wound dimension fields, as discussedwith respect to FIG. 2 . As also previously mentioned, the image capturedevice 302 may be a three-dimensional camera connected to the mobiledevice 110, which may also be used to capture wound images that may beused by the wound imaging and diagnostic application to automaticallydetermine one or more wound dimensions and upload the dimensions to theproper data fields in the wound imaging application. Additionally, theimage capture device 302 may be used to capture images of the tissuesite 106 over time, in order for a clinician, a patient, or otherinterested party to monitor the healing progress of the tissue site 106.Users, such as clinicians, may also have the ability to upload imagespreviously taken, which may be stored in a secure gallery on the mobiledevice 110. Tissue site images captured by the image capture device 302may each be stored in an image database, such as wound imaging database116, associated with the wound imaging and diagnostic application andtherapy network 100.

FIG. 3B illustrates an example embodiment of an image capture view 304of the GUI 200. In this example embodiment, the image capture view 304and an associated image capture software module of the wound imaging anddiagnostic application may provide a host of tools for aiding a user inobtaining high-quality wound images. For example, the image capture view304 may provide the user with one or more interfaces for guiding theuser with optimal camera orientation and placement during wound imagecapture. In some embodiments, the image capture view 304 may includetargeting elements 306 to aid in focusing on a centered, full-frameimage before using the image capture soft-button 308 to obtain and storethe image. Additionally, the image capture software module may providethe user with a variety of image capture tools as part of the imagecapture view 304. For example, the image capture view 304 may include aflash soft-button 310, which may provide the user with a variety ofdifferent light and/or flash options. A light analysis soft-button 312may also be included, which may perform an automated ambient lightassessment and may potentially alert the user of the need forsupplemental lighting or flash for capturing an ideal image of thewound. The image capture view 304 may also present the user with aprocessing soft-button 314, which may be selected to perform basic imagepre-processing steps (histogram equalization, white balance assessment,etc.), for assessing the quality of a captured image. Based on thispre-processing, the imaging software may make the determination that animage should be recaptured, and may present this determination to theuser as a pop-up message to the user on the image capture view 304. Theimage capture view 304 may also include a help soft-button 316, whichmay provide a user with a variety of instructions as well as options forcontacting a support center for further assistance.

FIG. 3C shows a screen shot of the illustrative GUI 200 with an exampleembodiment of a wound images view 320. In some embodiments, the woundimages view 320 may be accessed through the wound images field 226 ofthe patient information view 213. The wound images view 320 may displayone or more images of a single tissue site, or in some embodiments, ofmultiple tissue sites for a given patient. The wound images view 320 mayinclude a tissue site identification field 322, which may indicate thelocation or locations of the tissue site(s) of the images displayed aspart of the wound images view 320. The wound images view 320 may includea first image field 323 and a second image field 324. The first imagefield 323 may include a first image 326 of a single tissue site, whilethe second image field 324 may include a second image 328 of the sametissue site. In some embodiments, the first image field 323 and thesecond image field 324 may include images of different tissue sites,with the multiple tissue sites being identified in the tissue siteidentification field 322. By selecting the respective image of one ofthe image fields, a user may be presented with options for editing animage, enlarging the image for a more detailed view, drawing tracesaround the image, or performing other types of image modification ormanipulation. In some embodiments, the image fields, such as first imagefield 323 and second image field 324, may include data entry fields,such as notes fields 330 and 332, for allowing the user to enterdescriptions, measurements, or other information relevant to therespective image. Additionally, some embodiments of the wound imagesview 320 may include a messaging display 334. The messaging display 334may allow for users to send and receive secure messages, includingimages, to the support center 114, to external image-processing vendors,or to other clinicians.

FIG. 4 is a functional diagram illustrating capabilities of an exampleembodiment of a wound imaging and diagnostic application for use on atherapy network 100. The wound imaging and diagnostic application 400may be hosted by a secure server, which may be based on aHIPAA-compliant secure platform. The wound imaging and diagnosticapplication 400 may be operable on any suitable platform, including iOS,Android, and an HTML-based platform. The wound imaging and diagnosticapplication 400 may provide various functions depending on theparticular patient and type(s) of wounds presented.

As shown in FIG. 4 , the wound imaging and diagnostic application 400may provide multiple functional modules. The wound assessment module 402coordinates the gathering of wound images and descriptive informationassociated with the respective images. For example, the wound assessmentmodule 402 may coordinate with the descriptors and measurements module404 to provide an interface for a user to enter general patientinformation, as well as to collect and assess measurements and otherdescriptive information for one or more wounds. For example, a user,such as a clinician, may interface with multiple different views of theGUI 200 associated with the descriptors and measurements module 404 toenter dimensions and various other categories of information for the oneor more wounds. The wound images module 406 may enable the user tocollect one or more new images, using the associated image capturedevice 302 and the image capture view 304 of the GUI 200. The woundimages module 406 may provide the functional interface for a user tocapture, edit, and assess images of one or more wounds. In someembodiments, the wound images module 406 may also have the functionalityfor importing previously-taken images of one or more tissue sites, suchas wounds, for the given patient.

The wound assessment module 402 and the associated descriptors andmeasurements module 404 and wound images module 406 may communicate withthe image processing module 408. In some embodiments, the imageprocessing module 408 may provide the user with a list of possible imageprocessing functions that may be performed for the collected images ofthe one or more tissue sites. For example, the image processing module408 may coordinate with a multitude of additional modules that include avariety of image processing capabilities. In some embodiments, the imageprocessing module 408 may communicate with a pre-processing module 410,a color-space processing module 412, an active contouring module 414,and a photogrammetry module 416.

Additionally, the image processing module 408 may be in communicationwith a support center module 420, which may coordinate with a clinicalsupport module 422 and a technical support module 424. The collectivesupport center modules may provide a user with the option of seekingassistance with various aspects of the wound imaging and diagnosticapplication 400. Furthermore, the image processing module 408 maycommunicate with a processing support module 430. The processing supportmodule 430 may provide active interfaces to one or more modules forcommunicating with external image-processing vendors, applications, etc.Example image-processing vendors and/or applications include, but arenot limited to, 3D scanning/imaging application 432 for mobile devices.

Returning to the image processing module 408, a variety of core imageprocessing features may be included. For example, once thepre-processing steps have been completed by the pre-processing module410, the image processing module 408 may make the determination ofwhether the image meets initial quality thresholds. Included in theimage processing module 408 may be the software for passing the imagethrough one or more processes to elucidate key diagnostic featuresspecific to tissue sites, and particularly wounds. In some embodiments,the image processing module 408 may coordinate with the color-spacemodule 412. In some embodiments, the color-space module 412 may be usedto first convert a standard Red-Green-Blue (RGB) image into an alternatecolor-space, such as Hue-Saturation-Value (HSV),Cyan-Magenta-Yellow-Black (CMYK), and/or Lab. For example only, in imagedetection and processing, various features may be detected more easilyin different color-spaces. Some color-spaces (e.g., CMYK, Lab, etc.) mayfacilitate detection and processing of image features related to woundimaging. For example, skin coloration may be more easily detectable inthe CMYK and Lab color-spaces. The image processing module 408 may alsocoordinate with an active contouring module 414, which may includesoftware algorithms for performing a degree of low-level structuralanalysis of a wound image. Additionally, the image processing module 408may communicate with a photogrammetry module 416, for providing furtheranalysis of wound images.

FIG. 5 , collectively illustrates a wound image before and afterconversion to the CMYK space. FIG. 5A shows a RGB image of a freshlydebrided wound 506 located on the lower calf and ankle of a patient.FIG. 5B shows the imaged wound following conversion of the image to theCMYK space, illustrating the CMYK decomposition of the wound image. Insuch a CMYK decomposition, view 512 shows the image in the cyan space,view 516 shows features of the wound in the magenta space, view 520highlights aspects of the wound in the yellow space, and view 524 showsthe image of the wound in the black space. As shown in view 512 of FIG.5B, the wound 506 does not contribute to the Cyan channel. On the otherhand, the Magenta and Yellow channels illustrate how alternate colorrepresentations may highlight specific attributes of the appearance ofthe wound 506, as shown in view 516 and view 520, respectively, of FIG.5B.

The image processing module 408 may also include software for performinga degree of low-level structural analysis. For example, FIG. 5Cillustrates views of the intensity gradient decomposition of the woundimage shown in FIG. 5A. For example, view 528 illustrates the X-gradientview of the wound 506, while view 532 illustrates the Y-gradient view ofthe wound 506. The views of FIG. 5C also further illustrate some keycharacteristics of the wound 506. For example, in general, wound extentsare typified by rapid, discontinuous changes in image intensity thatreflect the chaotic texture of the wound bed. Image intensity generallycorresponds to the brightness of pixels in the selected-color space. Forexample, a low intensity pixel may have low brightness (e.g., a blackpixel) while a high intensity pixel may have high brightness (e.g., awhite pixel). Absolute intensity may not be relevant to detection ofindividual features. Conversely, relative brightness (i.e., theintensity of a pixel relative to neighboring pixels) may be indicativeof various features in an image. For example, relative intensity mayindicate discontinuities in an image that correspond to the perimeter ofa wound.

The image processing module 408 may also include the appropriatealgorithms for applying image filters for classifying regions of images.For example, skin and wound filters may be applied in order to classifyimage regions as skin, wound, or not relevant, as well as any otherclassifications that may be applied based on how different image filtersmay be tailored. In one example, the filters identify (e.g., byapplying/storing a label or other indicator) each pixel as a skin,wound, etc. If a first pixel is identified as “skin” but all surroundingpixels are identified as “wound,” the filters may modify theidentification of the first pixel from “skin” to “wound.” FIG. 6illustrates results of a skin classification filter operator, as appliedto the wound image of FIG. 5A, shown again as view 534 of FIG. 6 . Ascan be seen in FIG. 6 , following the filter application and processing,two separate views can be generated from the wound image of FIG. 5A. Inthis illustrative example, the first view is a wound classification view536, which highlights the portion or portions of the original woundimage that were determined to be wound tissue, such as wound portion540. In this illustrative example, the second view is a skinclassification view 538, which shows the portions of the original woundimage of FIG. 5A that were determined to be surrounding skin tissue,such as surrounding tissue portion 542.

In practice, wound tissue classification may be rather complex. Forexample, while the wound 506 pictured in FIG. 5A had been freshlydebrided and significant amounts of blood could be distinguished, FIG.7A provides some example illustrations of wound images that may be morechallenging for an automated skin filter operator to classify. Forexample, the views 702 and 712 of FIG. 7A illustrate wound images thatmay include multiple separate wound beds in the same image frame.Additionally, to further compound required classification analysis, eachwound bed in a single image frame may include a variety of tissue types.For example, as shown in the views 702 and 712, multiple different skinlayers 704 and 706 may be seen as well as underlying soft tissue 708.Further, portions of connective tissue 710, including tendons andligaments, may be part of the image frame, in addition to segments ofbone 711. Furthermore, as illustrated in view 720 and view 730, in somecases it may be difficult to accurately distinguish between an area of awound and the surrounding healthy skin due to the coloring and/ortexture of a peri-wound area being inconsistent with the surroundinghealthy skin. In other words, some wound image frames may present rathercomplex and/or unique challenges for wound perimeter identification.

FIG. 7B provides some additional example illustrations of wound imageframes that may present challenges for an automated skin filteroperator. For example, as shown in the views 740 and 750 of FIG. 7B,additional difficulties may arise in situations where the wound occupiesonly a small percentage of the wound image frame. Wound tissue that isnot uniformly colored or textured, as well as irregular wound bedfeatures, can also confound the automated skin filter operator.

To address the challenges presented in FIG. 7 , as well as others notspecifically illustrated, wound image processing algorithms may includemulti-modal techniques for producing clinically-relevant wound tissueclassifications. In some embodiments, such multi-modal techniques mayinclude color and intensity classification at the pixel (or path ofpixels) level, as well as other techniques cuing from higher-orderproperties, such as textural and structural features. For example, colorand intensity classification for a group of pixels may be indicative ofa wound perimeter and/or characteristics of regions within the wound(e.g., necrotic, healthy, etc.). Over time, changes in color andintensity classification may be indicative of changing characteristicsof the wound. For example, these classifications may indicate that thewound perimeter is decreasing, a ratio of healthy wound tissue tounhealthy wound tissue is increasing or decreasing, etc.

Accurate identification of a wound perimeter may be difficult due to thelarge variety of possible sizes, shapes, contexts, and overallappearance of wounds. To address the challenges with properlyidentifying wound perimeters according to the principles of the presentdisclosure, active contours (or snakes) are one method that may be usedas a tool for properly identifying the borders or edges of wounds. Inthese applications, active contours typically model (i.e., simulate) anelastic band that, when placed outside of the image object to becaptured, collapse inward to “hug” the object periphery. Activecontours, or snakes, may be framed as an iterative energy minimizationproblem, and are related to stochastic methods like simulated annealing.For example, when a simulated elastic band corresponding to an activecontour is stretched, the band may be considered to be in a high energystate. In other words, the band will tend toward a resting, unstretchedstate. In the context of wound imaging, the active contour may beconfigured into an initial state outlining the perimeter of the wound,which may be designated as (e.g., stored as) the resting state of theactive contour. In other words, the resting state of the active contourcorresponds to the perimeter of the wound. The state of the activecontour may be represented by an energy function that outputs energyvalues according to a current state of the active contour relative tothe resting state. For example, an output of the energy function may berelatively high when the active contour is outside of the woundperimeter, corresponding to a stretched state. Conversely, an output ofthe energy function is relatively low when the active contour is nearthe wound perimeter, corresponding to an unstretched state. As such, theenergy function may be designed to take into account image propertiesand features corresponding to a wound edge, perimeter, etc. toaccurately output a value representative of the configuration of theactive contour relative to actual wound perimeter.

For example, “energy minimization” refers to allowing the simulatedelastic band corresponding to the active contour to return to theresting state. In other words, in the context of wound imaging, theelastic band may initially be stretched into an ellipse or othersuitable shape enclosing the entire wound. Then, the simulated elasticband can be allowed to “snap” around the actual wound perimeter. Forexample, an iterative energy minimization process is applied to reducethe energy of the elastic band. More specifically, the iterative energyminimization process attempts to identify the actual pixelscorresponding to the wound perimeter and, accordingly, identify theposition of the active contour that would correspond to the woundperimeter. In one example, the iterative energy minimization processevaluates the energy function with respect to the pixel locations of thecurrent position of the active contour and then analyzes regions aroundeach of these pixels. For example, for a pixel at a given point P, theprocess determines whether moving the pixel to a different point withinits respective region would reduce the overall energy of the activecontour (i.e., whether the new point is closer to the actual woundperimeter as indicated by, for example, identified featurescorresponding to the wound perimeter). If so, then the correspondingportion of the active contour is moved to the new point within theregion to reduce the overall energy of the active contour. The processis repeated for each pixel of the active contour until no furthermodifications will reduce the energy of the active contour.

In some embodiments, the snake may respond to internal and external“pressures” in order to find a low-energy state. Regularizationparameters of curvature and smoothness may enforce physical propertiesof the elastic band and may make the band resistant to breaking.External properties consistent with wound peripheries may be modeledsuch that they represent low-energy states. For example, multiplelow-level parameters may be suggested in the wound image of FIG. 5A,such as red pixels, as well as pixels strong in magenta and yellow,however not cyan. Additionally, the gradient images, such as thosepictured in FIG. 5C, may suggest that higher-order structures based ongradients in image intensity may be useful. Typically, in practice, theexternal properties that shape the snake are usually chosen understatistical guidance.

Referring now collectively to FIG. 8 , an illustrative embodiment of theprocess of applying active contours to obtain high-fidelity woundperimeters is shown. As shown in first view 802 of FIG. 8A, a user ofthe image processing software may touch the image 804 around a wound 806to set a variable number of control points 808 (or, in some examples,draw or trace a continuous line) in order to establish an initial, roughoutline around the wound (e.g., a rough-bounding polygon). Asillustrated in the second view 810 of FIG. 8A, the user may thendouble-tap the screen in order to initialize the automatic activecontour lines 812 from the provided control points 808, creating aninitial active contour, or snake, 814 from the user-provided controlpoints 808.

Following now to FIG. 8B, the process of iterative convergence of theinitial snake 814 may be shown in the example illustrations. Once theinitial contour of the snake 814 has been identified, the imageprocessing software may statistically analyze regions of the imageoutside of the active contour and compare them to regions within thecontour. In this manner, the software may be able to automaticallyrefine the snake 814 to more closely track the perimeter 822 of thewound 806, as shown in the first view 820 of FIG. 8B. The imageprocessing software's interface, such as the touchscreen GUI 200 mayallow the user to adjust the snake 814 through tap-and-drag gestures.For example, in some embodiments, the touchscreen GUI 200 may allow theuser to select and unselect regions, expand and contract portions of thesnake 814, as well as other interactive and guided adjustments. In someembodiments, a double-tap on the touchscreen GUI 200 by the user maycommit the user's rough measurement of the perimeter 822 of the wound806. Additionally, in some embodiments, the image processing softwaremay permit the user to select between a contractive snake, as depictedin FIGS. 8-12 , and an inflationary snake. For example, an inflationarysnake may refer to a snake in which the contour is initialized with asingle tap within the interior of the wound 806. This form of snake maythen be expanded to identify the wound perimeter during energyminimization. The use of an inflationary snake may be particularlyappropriate for smaller, symmetric wounds, such as many diabetic footulcers.

The first view 820 of FIG. 8B illustrates how after approximately 75iterations of active contour processing, the snake 814 has converged tothe upper perimeter 824 of the wound 806. Further, after approximately150 iterations, as shown in the second view 830 of FIG. 8B, the snake814 has converged to most of the perimeter 822 of wound 806. Continuingwith FIG. 8C, further steps of the iterative convergence process of thesnake 814 may be seen. Thus, the first view 840 of FIG. 8C shows how,after 225 iterations, the snake 814 has converged to almost all of theperimeter 822 of the wound 806, while the second view 850 of FIG. 8Cillustrates how, after 261 iterations, the snake 814 has convergedessentially entirely to the perimeter 822 of wound 806. FIG. 8D showsthe same example wound 806 with the snake 814 locked onto the perimeter822 of the wound 806 and stabilized. In the example view 860, theperimeter 822 of the wound 806 is highlighted in blue by the imageprocessing software, and the wound interior 862 is highlighted in red.In this particular example image, the area of the shaded region, whichcorresponds to the wound interior 862, is 171,448 pixels.

As shown in FIG. 9 , throughout the iterative convergence process (e.g.,an iterative energy minimization process as described above), the degreeof convergence may be charted and tracked in order to determine whenconvergence has been completed. For example, the graph 900 of FIG. 9shows that convergence is indicated when the smoothed contour energystabilizes. Thus, FIG. 9 illustrates a depiction of an energyminimization sequence of the snake 814. When convergence is complete(and, therefore, the active contour is in the resting statecorresponding to the wound perimeter), the user may further modify theposition of the active contour using the touchscreen. For example, ifthe user recognizes that a portion of the active contour is notconsistent with the actual wound perimeter, the user may use the touchscreen (e.g., using tap-and-drag gestures) to adjust the active contouraccordingly. The iterative convergence process may then be repeatedusing this feedback.

FIG. 10 , collectively, illustrates an additional example of the imageprocessing software's capability of applying the iterative convergenceprocess to a wound image 1004. The wound image 1004 shown in FIG. 10 ,collectively, may be an image of a wound 1006. As shown in a first view1002 of FIG. 10A, a user has tapped the touchscreen displaying a GUI 200in order to set a variable number of control points 1008 in order toestablish an initial snake 1014, as shown in the second view 1012 ofFIG. 10A. FIG. 10B illustrates how once the user has double-tapped thetouchscreen displaying the GUI 200, the automated wound perimeteridentification functionality of the image processing software may beactivated. As shown by comparing FIGS. 10A and 10B, the initial snake1014 of FIG. 10B was created from the user-provided points 1008 in FIG.10A. The first and second views, 1020 and 1030, respectively, of FIG.10B show the progress of the convergence of the snake 1014 to theperimeter 1022 of the wound 1006 after approximately 75 iterations and150 iterations, respectively. As shown in the first view 1020 of FIG.10B, the active contour of the snake 1014 has converged uniformly to theperimeter 1022 of the wound 1006, while the second view 1030 of FIG. 10Bshows how the active contour of the snake 1014 has converged to most ofthe perimeter 1022 of the wound 1006. FIG. 10C illustrates the continuedsteps of the active contour convergence process. For example, the view1040 of FIG. 10C illustrates how after 235 iterations of active contourprocessing, the snake 1014 has locked onto the perimeter 1022 of thewound 1006 and has been stabilized. In some embodiments, the perimeter1022 of the wound 1006 may be highlighted in blue, and the interior 1062of the wound 1006 may be highlighted in red. For example, in thisillustrative example, the area of the shaded region corresponding to theinterior 1062 of the wound 1006 has a size of 228,627 pixels. Similar toFIG. 9 , FIG. 11 illustrates how convergence may be graphicallyindicated, when the smoothed contour energy stabilizes, as shown inregion 1102 of the graph 1100.

FIG. 12 , collectively, illustrates yet another example of the iterativeconvergence process of the image processing software, applied to a wound1206. Similarly to the examples discussed with respect to FIGS. 8 and 10, the user may tap the touchscreen displaying a GUI 200 in order to seta variable number of control points 1208 for establishing arough-bounding polygon, as illustrated in the first view 1202 of FIG.12A. The user may then double-tap the touchscreen to activate theautomated wound perimeter identification functionality of the imageprocessing software, and to generate the snake 1214, as shown in thesecond view 1210. The first and second views, 1220 and 1230,respectively, of FIG. 12B once again illustrate the progress of theconvergence of the snake 1214 to the perimeter 1222 of the wound 1206after 75 and 150 iterations, respectively. Now referring to FIG. 12C,the view 1240 shows how, in this illustrative example, the snake 1214has locked onto the perimeter 1222 of the wound 1206 and has beenstabilized. Similar to the examples of FIGS. 8 and 10 , the perimeter1222 of the wound 1206 may be highlighted in blue, and the interior 1262of the wound 1206 may be highlighted in red. In this illustrativeexample, the area of the shaded region corresponding to the interior1262 of the wound 1206 includes 66,075 pixels, thus indicating that thearea of the wound 1206 may be considerably smaller than that of thewounds 806 and 1006 of FIGS. 8 and 10 , respectively. Once again, theconvergence of the snake 1214 may be graphically indicated by noticingwhen the smoothed contour energy stabilizes as shown in region 1302 ofthe graph 1300 of FIG. 13 .

As indicated with respect to the explanation of FIGS. 8-13 , the processfor identifying the perimeter of a wound may provide an estimate of thewound area in units of square pixels. In order to transform this areameasurement from square pixels into a clinically-relevant measure ofcentimeters, the image processing software may also be equipped with thecapacity for detecting and using image scale-identification markers. Forexample, an example of an image scale-identification marker may be the 2cm×2 cm square on a wound ruler, which may be available from KineticConcepts Inc., of San Antonio, Tex. The marker may have a shape thatfacilitates identification of the size of the image (e.g., a square,such as a square with 2 cm sides) while also facilitating conversion todifferent size units. Using such a marker, the image processing softwaremay be able to transform pixel area measurements into real-worldmeasurements. For example, the marker may be sized such that apredetermined amount of pixels fit within the marker, and each pixel (orstring of pixels) is correlated to a real-world measurement. For exampleonly, 200 pixels may correspond to 2 cm. Referring now to FIG. 14 , theresult of the use of an image filter to automatically identify theextents of a wound ruler may be seen. Additionally, the image processingsoftware may allow the user to refine the marker identification, aspotentially required. For example, characteristics such as size andcolor of the scale marker preferably allow the scale marker to be moreeasily distinguishable from the image itself.

In addition to the examples of the previously-discussed figures, thereare a number of algorithms as well as techniques that may be used toimprove or refine automated wound periphery measurements. For example,advanced classification methods, such as support vector machines (SVMs)may be used. Additionally, artificial neural networks (ANNs) may also beapplied to assist with refining the wound periphery measurements. Insome embodiments, additional algorithms and techniques may be used toassist with wound segmentation. For example, a variety of probabilisticalgorithms may also be utilized. Alternatively or additionally, woundtexture analysis may be used to aid with wound segmentation. Such woundtexture analysis techniques may include gray-level co-occurrence matrix(GLCM), wavelets, as well as other processes. As an example, a techniquesuch as GLCM and/or wavelets may be used to identify certain features ofa wound image such as textures, edges, etc., while techniques such asSVMs and/or ANNs may be used to analyze those identified features todetermine wound regions. In one example, the identified features maycorrespond to regions that are red and not smooth, while furtherprocessing performed by an SVM, ANN, etc. classifies regions havingthose features as wound regions (i.e., correlating red and not smoothfeatures to a wound region rather than healthy skin).

Embodiments of the wound imaging and diagnostic application may befurther customized to include additional functional modules for offeringfurther capabilities and services to users of the application. Forexample, one or more functional modules related to wound imaging may beintegrated. The wound imaging and diagnostic application may alsoinclude training modules, which may be configured to offer clinicianstutorials on utilizing the application as well as training videos forprocessing more complex tissue sites. Links to external reference guidesmay also be provided via links in the application in order for ease ofuse.

The systems, apparatuses, and methods described herein may providesignificant advantages. Currently, widely-practiced manual technologiesand methods for wound area assessment are known to be subjective andoften yield coarse estimates of wound area. For example, as previouslymentioned, color is a prime indicator of wound healing that is commonlyused in clinical settings. However, one problem with usingcolor-identification is that color appearance can often be altereddepending on lighting conditions. For example, a wound underincandescent lighting may have a very different color appearance from awound under fluorescent lighting. Furthermore, different clinicians mayhave different degrees of color perception. For example, while oneclinician may have strong color perception, another clinician may becolor blind in one or more colors, thereby creating a situation wherethe two clinicians construe different interpretations of the color, andtherefore type, of wound tissue.

Furthermore, the systems and methods described herein may offerparticular advantages over current techniques for identifying andmeasuring different types of wound tissue. For example, one currenttechnique includes placing a clear film over a wound and using asoft-tipped pen to color different wound tissues on the film, therebymaking a record of the wound tissues. This process may be repeated torecord wound healing over time. However, this process often suffers dueto lighting conditions, color sensitivity of clinicians, limitations ofthe ability of a clinician to accurately draw on the clear film, andinherent problems associated with contacting the film onto the woundtissue. Another technique includes making an outline of the wound on thefilm, scanning the image into a computer, and then drawing an estimationof the different wound tissue on the computer. However, this techniquealso suffers from inaccuracy.

In contrast, calibrated, automated image processing systems and methods,such as the systems and methods described herein may increase wound areameasurement accuracy. While current practice involves probing the woundbed to determine wound depth, which often can lead to pain or discomfortfor the patient, using a three-dimensional camera to image the wound canautomatically calculate many of these dimensions. As a result, much ofthe need for making physical contact with the wound may be avoided.Given these observations, as well as the widespread use of smartphonesand tablets that are equipped with high-resolution cameras and robust,multi-core processors, the opportunity exists for developing a platformfor improving in-field wound care. As such, a convenient and intuitivewound imaging and analysis system can reduce subjectivity and improveefficiency in routine wound assessment. Furthermore, the benefits of thepresent invention may be substantially scaled should the invention beapplied in a telemedicine wound management program.

While shown in a few illustrative embodiments, a person having ordinaryskill in the art will recognize that the systems, apparatuses, andmethods described herein are susceptible to various changes andmodifications. Moreover, descriptions of various alternatives usingterms such as “or” do not require mutual exclusivity unless clearlyrequired by the context, and the indefinite articles “a” or “an” do notlimit the subject to a single instance unless clearly required by thecontext. Components may be also be combined or eliminated in variousconfigurations for purposes of sale, manufacture, assembly, or use.

The appended claims set forth novel and inventive aspects of the subjectmatter described above, but the claims may also encompass additionalsubject matter not specifically recited in detail. For example, certainfeatures, elements, or aspects may be omitted from the claims if notnecessary to distinguish the novel and inventive features from what isalready known to a person having ordinary skill in the art. Features,elements, and aspects described herein may also be combined or replacedby alternative features serving the same, equivalent, or similar purposewithout departing from the scope of the invention defined by theappended claims.

What is claimed is:
 1. A non-transitory computer-readable mediumcomprising executable code, wherein the executable code comprises:receiving, at an active contouring module, a wound image; identifying,at the active contouring module, features of the wound image on oppositesides of an initial outline of a wound; and calculating an actualperimeter of the wound based on the initial outline of the wound and theidentified features of the wound image.
 2. The non-transitorycomputer-readable medium of claim 1, wherein the executable code furthercomprises: generating a graphical representation of the actual perimeterof the wound for output to a display screen.
 3. The non-transitorycomputer-readable medium of claim 1, wherein the executable code furthercomprises: calculating a wound area based on the actual perimeter of thewound.
 4. The non-transitory computer-readable medium of claim 1,wherein the executable code further comprises: identifying a number ofpixels enclosed by the actual perimeter of the wound; detecting animage-scale identification marker present in the wound image;identifying a size of each pixel based on the image-scale identificationmarker; and calculating a wound area based on the number of pixelsenclosed by the actual perimeter of the wound.
 5. The non-transitorycomputer-readable medium of claim 1, wherein the executable code furthercomprises: initializing an active contour model by defining the initialoutline of the wound as an initial active contour; performing aniterative energy minimization process on the initial active contour todetermine a final active contour; and storing the final active contouras the actual perimeter of the wound.
 6. The non-transitorycomputer-readable medium of claim 5, wherein the executable code furthercomprises: converting the wound image from an RGB color space to a CMYKcolor space; and decomposing the wound image into a magenta componentand a yellow component.
 7. The non-transitory computer-readable mediumof claim 6, wherein the executable code further comprises: defining themagenta component as a low-energy state in the iterative energyminimization process.
 8. The non-transitory computer-readable medium ofclaim 6, wherein the executable code further comprises: defining theyellow component as a low-energy state in the iterative energyminimization process.
 9. The non-transitory computer-readable medium ofclaim 1, wherein the executable code further comprises: creating agradient image in an x-direction from the wound image; and calculatingthe actual perimeter of the wound by identifying areas of high gradientfrom the gradient image.
 10. The non-transitory computer-readable mediumof claim 1, wherein the executable code further comprises: creating agradient image in a y-direction from the wound image; and calculatingthe actual perimeter of the wound by identifying areas of high gradientfrom the gradient image.
 11. A method for processing a wound image,comprising: receiving, at an active contouring module, the wound image;identifying, at the active contouring module, features of the woundimage on opposite sides of an initial outline of a wound; andcalculating an actual perimeter of the wound based on the initialoutline of the wound and the identified features of the wound image. 12.The method of claim 11, further comprising: generating a graphicalrepresentation of the actual perimeter of the wound for output to adisplay screen.
 13. The method of claim 11, further comprising:calculating a wound area based on the actual perimeter of the wound. 14.The method of claim 11, further comprising: identifying a number ofpixels enclosed by the actual perimeter of the wound; detecting animage-scale identification marker present in the wound image;identifying a size of each pixel based on the image-scale identificationmarker; and calculating a wound area based on the number of pixelsenclosed by the actual perimeter of the wound.
 15. The method of claim11, further comprising: initializing an active contour model by definingthe initial outline of the wound as an initial active contour;performing an iterative energy minimization process on the initialactive contour to determine a final active contour; and storing thefinal active contour as the actual perimeter of the wound.
 16. Themethod of claim 15, further comprising: converting the wound image froman RGB color space to a CMYK color space; and decomposing the woundimage into a magenta component and a yellow component.
 17. The method ofclaim 16, further comprising: defining the magenta component as alow-energy state in the iterative energy minimization process.
 18. Themethod of claim 16, further comprising: defining the yellow component asa low-energy state in the iterative energy minimization process.
 19. Themethod of claim 11, further comprising: creating a gradient image in anx-direction from the wound image; and calculating the actual perimeterof the wound by identifying areas of high gradient from the gradientimage.
 20. The method of claim 11, further comprising: creating agradient image in a y-direction from the wound image; and calculatingthe actual perimeter of the wound by identifying areas of high gradientfrom the gradient image.